The present invention relates to an information processing system, and more particularly to stream data processing by distributed processing of a plurality of computers.
Conventional data processing techniques generally utilize relational database (hereinafter abbreviated to RDB) techniques. The problems arising when a large amount of data is processed in a short response time by using RDB are mainly to store data once in a disk drive slower than a main storage and to apply a query to stored data in a batch processing mode. The influence of a response time prolonged by storing data in a disk drive is mitigated because in recent years a cost of a main storage becomes low and techniques of storing data in a main storage are prevailing. However, a response time is prolonged by applying a query in a batch processing mode to RDB.
Stream data processing techniques solve the disadvantages of RDB by registering queries in a system in advance, and when data arrives at the system, processing a query by a differential approach. Further, a query can be written easily by utilizing declarative query definition language called CQL.
The stream data processing techniques provide efficient conversion from stream data to relational data, by using a sliding window. Further, the stream data processing techniques utilize query description language called CQL which is obtained by adding SQL with a conversion operation for stream data and relational data, in order to easily write a process similar to SQL for relational data. Furthermore, the stream data processing techniques perform an aggregate operation by a differential approach in main memory, in order to execute a process for relational data, particularly an aggregate process, at high speed.
These techniques are disclosed in A. Arasu et al, “STREAM: The Stanford Stream Data Manager” IEEE Data Engineering Bulletin, Vol. 26, 2003.